This report shows the complete 4-step iterative process: 1. Planner: Strategic planning and task decomposition 2. Developer: Initial implementation 3. Auditor: Review and feedback 4. Developer: Refined implementation
Status: β Failed
Numerical Features Summary Statistics:
Area Perimeter Major_Axis_Length Minor_Axis_Length \
count 2500.000000 2500.000000 2500.000000 2500.000000
mean 80658.220800 1130.279015 456.601840 225.794921
std 13664.510228 109.256418 56.235704 23.297245
min 47939.000000 868.485000 320.844600 152.171800
25% 70765.000000 1048.829750 414.957850 211.245925
50% 79076.000000 1123.672000 449.496600 224.703100
75% 89757.500000 1203.340500 492.737650 240.672875
max 136574.000000 1559.450000 661.911300 305.818000
Convex_Area Equiv_Diameter Eccentricity Solidity Extent \
count 2500.000000 2500.000000 2500.000000 2500.000000 2500.000000
mean 81508.084400 319.334230 0.860879 0.989492 0.693205
std 13764.092788 26.891920 0.045167 0.003494 0.060914
min 48366.000000 247.058400 0.492100 0.918600 0.468000
25% 71512.000000 300.167975 0.831700 0.988300 0.658900
50% 79872.000000 317.305350 0.863700 0.990300 0.713050
75% 90797.750000 338.057375 0.897025 0.991500 0.740225
max 138384.000000 417.002900 0.948100 0.994400 0.829600
Roundness Aspect_Ration Compactness
count 2500.000000 2500.000000 2500.000000
mean 0.791533 2.041702 0.704121
std 0.055924 0.315997 0.053067
min 0.554600 1.148700 0.560800
25% 0.751900 1.801050 0.663475
50% 0.797750 1.984200 0.707700
75% 0.834325 2.262075 0.743500
max 0.939600 3.144400 0.904900
Categorical Feature 'Class' Summary:
Unique values: 2
Frequency distribution:
Class
ΓerΓ§evelik 1300
ΓrgΓΌp Sivrisi 1200
Name: count, dtype: int64
Status: β Failed
Missing Values per Column:
Area 0
Perimeter 0
Major_Axis_Length 0
Minor_Axis_Length 0
Convex_Area 0
Equiv_Diameter 0
Eccentricity 0
Solidity 0
Extent 0
Roundness 0
Aspect_Ration 0
Compactness 0
Class 0
dtype: int64
Data Types of Each Column:
Area int64
Perimeter float64
Major_Axis_Length float64
Minor_Axis_Length float64
Convex_Area int64
Equiv_Diameter float64
Eccentricity float64
Solidity float64
Extent float64
Roundness float64
Aspect_Ration float64
Compactness float64
Class object
dtype: object
Status: β Failed
Skewness of Numerical Features:
Area 0.495701
Perimeter 0.414290
Major_Axis_Length 0.502678
Minor_Axis_Length 0.104241
Convex_Area 0.493719
Equiv_Diameter 0.271704
Eccentricity -0.748174
Solidity -5.687594
Extent -1.025952
Roundness -0.372463
Aspect_Ration 0.547902
Compactness -0.062339
dtype: float64
Kurtosis (Excess) of Numerical Features:
Area 0.126339
Perimeter -0.024205
Major_Axis_Length -0.018057
Minor_Axis_Length 0.070689
Convex_Area 0.120381
Equiv_Diameter -0.148808
Eccentricity 1.788224
Solidity 80.957095
Extent 0.421733
Roundness -0.241156
Aspect_Ration -0.205354
Compactness -0.502231
dtype: float64
Status: β Failed
Correlation Matrix among Numerical Features:
Area Perimeter Major_Axis_Length Minor_Axis_Length \
Area 1.000000 0.928548 0.789133 0.685304
Perimeter 0.928548 1.000000 0.946181 0.392913
Major_Axis_Length 0.789133 0.946181 1.000000 0.099376
Minor_Axis_Length 0.685304 0.392913 0.099376 1.000000
Convex_Area 0.999806 0.929971 0.789061 0.685634
Equiv_Diameter 0.998464 0.928055 0.787078 0.690020
Eccentricity 0.159624 0.464601 0.704287 -0.590877
Solidity 0.158388 0.065340 0.119291 0.090915
Extent -0.014018 -0.140600 -0.214990 0.233576
Roundness -0.149378 -0.500968 -0.684972 0.558566
Aspect_Ration 0.159960 0.487880 0.729156 -0.598475
Compactness -0.160438 -0.484440 -0.726958 0.603441
Convex_Area Equiv_Diameter Eccentricity Solidity \
Area 0.999806 0.998464 0.159624 0.158388
Perimeter 0.929971 0.928055 0.464601 0.065340
Major_Axis_Length 0.789061 0.787078 0.704287 0.119291
Minor_Axis_Length 0.685634 0.690020 -0.590877 0.090915
Convex_Area 1.000000 0.998289 0.159156 0.139178
Equiv_Diameter 0.998289 1.000000 0.156246 0.159454
Eccentricity 0.159156 0.156246 1.000000 0.043991
Solidity 0.139178 0.159454 0.043991 1.000000
Extent -0.015449 -0.010970 -0.327316 0.067537
Roundness -0.153615 -0.145313 -0.890651 0.200836
Aspect_Ration 0.159822 0.155762 0.950225 0.026410
Compactness -0.160432 -0.156411 -0.981689 -0.019967
Extent Roundness Aspect_Ration Compactness
Area -0.014018 -0.149378 0.159960 -0.160438
Perimeter -0.140600 -0.500968 0.487880 -0.484440
Major_Axis_Length -0.214990 -0.684972 0.729156 -0.726958
Minor_Axis_Length 0.233576 0.558566 -0.598475 0.603441
Convex_Area -0.015449 -0.153615 0.159822 -0.160432
Equiv_Diameter -0.010970 -0.145313 0.155762 -0.156411
Eccentricity -0.327316 -0.890651 0.950225 -0.981689
Solidity 0.067537 0.200836 0.026410 -0.019967
Extent 1.000000 0.352338 -0.329933 0.336984
Roundness 0.352338 1.000000 -0.935233 0.933308
Aspect_Ration -0.329933 -0.935233 1.000000 -0.990778
Compactness 0.336984 0.933308 -0.990778 1.000000
Status: β Failed
Class Distribution Counts:
Class
ΓerΓ§evelik 1300
ΓrgΓΌp Sivrisi 1200
Name: count, dtype: int64
Status: β Failed
Outlier Detection and Analysis in Numerical Features
Outlier Summary (IQR and Z-score methods):
Feature IQR_Outliers_Count IQR_Outliers_% Zscore_Outliers_Count Zscore_Outliers_% Mean_All Std_All Mean_wo_Outliers Std_wo_Outliers
Area 18 0.72 13 0.52 80658.220800 13664.510228 80331.083400 13152.687709
Perimeter 16 0.64 8 0.32 1130.279015 109.256418 1128.082581 106.080663
Major_Axis_Length 21 0.84 8 0.32 456.601840 56.235704 455.168829 54.250506
Minor_Axis_Length 30 1.20 9 0.36 225.794921 23.297245 225.731180 22.258129
Convex_Area 17 0.68 13 0.52 81508.084400 13764.092788 81194.389448 13269.303919
Equiv_Diameter 13 0.52 9 0.36 319.334230 26.891920 318.891348 26.248731
Eccentricity 18 0.72 14 0.56 0.860879 0.045167 0.862081 0.042827
Solidity 103 4.12 29 1.16 0.989492 0.003494 0.989957 0.002157
Extent 46 1.84 13 0.52 0.693205 0.060914 0.696548 0.056276
Roundness 5 0.20 4 0.16 0.791533 0.055924 0.791916 0.055307
Aspect_Ration 11 0.44 8 0.32 2.041702 0.315997 2.037341 0.309768
Compactness 2 0.08 2 0.08 0.704121 0.053067 0.703975 0.052836
Analysis Notes:
- IQR method identifies moderate outliers based on quartiles.
- Z-score method identifies extreme outliers beyond 3 standard deviations.
- Comparing mean and std with and without outliers shows their influence on distribution.
- Features with substantial outliers may require treatment such as capping or removal.
- Visualizations help confirm the presence and spread of outliers for each feature.
Status: β Failed
### Scatter Plots: Selected Numerical Features vs Target Class ###
- Scatter plot of Perimeter vs Area shows how classes separate or cluster in this feature space.
- Scatter plot of Minor_Axis_Length vs Major_Axis_Length shows how classes separate or cluster in this feature space.
- Scatter plot of Equiv_Diameter vs Convex_Area shows how classes separate or cluster in this feature space.
- Scatter plot of Aspect_Ration vs Roundness shows how classes separate or cluster in this feature space.
### Pair Plot: All Numerical Features Colored by Class ###
- Pair plot reveals pairwise relationships and class separability patterns across all numerical features.
### Violin Plots: Distribution of Numerical Features by Class ###
- Violin plot of Area shows distribution shape and differences between classes.
- Violin plot of Perimeter shows distribution shape and differences between classes.
- Violin plot of Major_Axis_Length shows distribution shape and differences between classes.
- Violin plot of Minor_Axis_Length shows distribution shape and differences between classes.
- Violin plot of Convex_Area shows distribution shape and differences between classes.
- Violin plot of Equiv_Diameter shows distribution shape and differences between classes.
- Violin plot of Eccentricity shows distribution shape and differences between classes.
- Violin plot of Solidity shows distribution shape and differences between classes.
- Violin plot of Extent shows distribution shape and differences between classes.
- Violin plot of Roundness shows distribution shape and differences between classes.
- Violin plot of Aspect_Ration shows distribution shape and differences between classes.
- Violin plot of Compactness shows distribution shape and differences between classes.
Visualizations complete. Review plots for patterns such as clustering, separability, and distribution differences between classes.
Status: β Failed
### Data Quality and Consistency Checks ###
Number of duplicate records in the dataset: 0
---
Checking numerical feature value ranges against min and max from summary statistics:
Feature Count_Below_Min Count_Above_Max
Area 0 0
Perimeter 0 0
Major_Axis_Length 0 0
Minor_Axis_Length 0 0
Convex_Area 0 0
Equiv_Diameter 0 0
Eccentricity 0 0
Solidity 0 0
Extent 0 0
Roundness 0 0
Aspect_Ration 0 0
Compactness 0 0
No values should be below min or above max as these are dataset min/max.
Logical consistency checks between related features:
Records where Area > Convex_Area: 0
Records where Perimeter < Major_Axis_Length: 0
Records where Perimeter < Minor_Axis_Length: 0
Records where Area differs from ellipse area approximation by >30%: 0
Records where Aspect_Ration differs from Major_Axis_Length/Minor_Axis_Length by >0.1: 0
Summary of logical inconsistencies:
- Area > Convex_Area: 0 records
- Perimeter < Major_Axis_Length: 0 records
- Perimeter < Minor_Axis_Length: 0 records
- Area vs Ellipse area difference >30%: 0 records
- Aspect_Ration inconsistent with axes ratio >0.1: 0 records
